This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO42-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO3, Mn and total coliform were associated with optimal water pH while SO42-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.

Introduction The coronavirus 2019 (COVID-19) has overwhelmed the health systems of several countries, particularly those within the African region. Notwithstanding, the relationship between health systems and the magnitude of COVID-19 in African countries have not received research attention. In this study, we investigated the relationship between the pervasiveness of the pandemic across African countries and their Global Health Security Index (GHSI) scores. Materials and methods The study included 54 countries in five regions viz Western (16); Eastern (18); Middle (8); Northern (7); and Southern (5) Africa. The outcome variables in this study were the total confirmed COVID-19 cases (per million); total recoveries (per million); and the total deaths (per million). The data were subjected to Spearman’s rank-order (Spearman’s rho) correlation to determine the monotonic relationship between each of the predictor variables and the outcome variables. The predictor variables that showed a monotonic relationship with the outcome were used to predict respective outcome variables using multiple regressions. The statistical analysis was conducted at a significance level of 0.05. Results Our results indicate that total number of COVID-19 cases (per million) has strong correlations (rs >0.5) with the median age; aged 65 older; aged 70 older; GDP per capita; number of hospital beds per thousand; Human Development Index (HDI); recoveries (per million); and the overall risk environment of a country. All these factors including the country’s commitments to improving national capacity were related to the total number of deaths (per million). Also, strong correlations existed between the total recoveries (per million) and the total number of positive cases; total deaths (per million); median age; aged 70 older; GDP per capita; the number of hospital beds (per thousand); and HDI. The fitted regression models showed strong predictive powers (R-squared>99%) of the variances in the total number of COVID-19 cases (per million); total number of deaths (per million); and the total recoveries (per million). Conclusions The findings from this study suggest that patient-level characteristics such as ageing population (i.e., 65+), poverty, underlying co-morbidities–cardiovascular disease (e.g., hypertension), and diabetes through unhealthy behaviours like smoking as well as hospital care (i.e., beds per thousand) can help explain COVID-19 confirmed cases and mortality rates in Africa. Aside from these, other determinants (e.g., population density, the ability of detection, prevention and control) also affect COVID-19 prevalence, deaths and recoveries within African countries and sub-regions.

Our results indicate that total number of COVID-19 cases (per million) has strong correlations (r s >0.5) with the median age; aged 65 older; aged 70 older; GDP per capita; number of hospital beds per thousand; Human Development Index (HDI); recoveries (per million); and the overall risk environment of a country. All these factors including the country's commitments to improving national capacity were related to the total number of deaths (per million). Also, strong correlations existed between the total recoveries (per million) and the total number of positive cases; total deaths (per million); median age; aged 70 older; GDP per capita; the number of hospital beds (per thousand); and HDI. The fitted regression models showed strong predictive powers (R-squared>99%) of the variances in the total number of COVID-19 cases (per million); total number of deaths (per million); and the total recoveries (per million). ConclusionsThe findings from this study suggest that patient-level characteristics such as ageing population (i.e., 65 + ), poverty, underlying co-morbidities-cardiovascular disease (e.g., hypertension), and diabetes through unhealthy behaviours like smoking as well as hospital care (i.e., beds per thousand) can help explain COVID-19 confirmed cases and mortality rates in Africa. Aside from these, other determinants (e.g., population density, the ability of detection, prevention and control) also affect COVID-19 prevalence, deaths and recoveries within African countries and sub-regions. PLOS ONEStrengths of healthcare systems associated with COVID-19 prevalence, deaths and recoveries

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The occurrence of pollution indicator bacteria (total and faecal coliform) has been used as a sanitary parameter for evaluating the quality of drinking water. It is known that these indicators are associated with disease causing organisms which are of great concern to public health. This study assessed the relationship between coliform bacteria and water geochemistry in surface and ground water systems in the Tarkwa mining area using logistic regression models. In surface water sources, higher values of chloride (OR = 0.891, p<005), phosphates (OR = 0.452, p<0.05), pH (OR = 0.174, p<0.05) and zinc (OR = 0.001, p<0.05) were associated with lower odds of faecal coliform contamination. In groundwater sources, higher values of phosphates (OR = 0.043, p<0.001), total dissolved solids (OR = 0.858, p<0.05), turbidity (OR = 0.996, p<0.05) and nickel (OR = 6.09E-07, p<0.05) implied non-contamination by faecal coliform. However, higher values of electrical conductivity (OR = 1.097, p<0.05), nitrates (OR = 1.191, p<0.05) and total suspended solids (OR = 1.023, p<0.05) were associated with higher odds of faecal coliform contamination of groundwater sources. Nitrates and total suspended solids, in this case, were completely mediated by the heavy metals. For total coliform in surface water systems, higher values of magnesium (OR = 1.070, p<0.05) was associated with higher odds of total coliform contamination while higher values of phosphates (OR = 0.968, p<0.05) was associated with lower odds of total coliform contamination although the presence of heavy metals completely mediated these relationships. For ground water systems, higher values of pH (OR = 0.083, p<0.05), phosphates (OR = 0.092, p<0.05), turbidity (OR = 0.950, p<0.05) and chloride (OR = 0.860, p<0.05) were associated with lower odds of total coliform contamination. However, higher values of total suspended solids (OR = 1.054, p<0.05) and nitrates (OR = 1.069, p<0.05) implied contamination of total coliform in ground water sources. The relationship between nitrates and total coliform were mediated by the heavy metals. This study establishes the need to monitor, manage and remediate surface and ground water sources for potential disease causing microbes in ways that takes into consideration the factors that create different conditions in the two water systems. This study validates the usefulness of statistical models as tools for preventing surface and ground water contamination.